Can I Run Qwen3 VL 8B Thinking on a NVIDIA RTX 5060 Ti 8GB?
Runs at Q5_K_M — good quality with reasonable headroom.
9 quantizations fit your 8.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q5_K_MBEST | 7.3 GB | 8.8 GB | 5.8 GB | +0.8 GB |
| Q5_K_S | 7.1 GB | 8.6 GB | 5.7 GB | +0.9 GB |
| Q4_1 | 6.5 GB | 8.0 GB | 5.3 GB | +1.5 GB |
| Q4_K_M | 6.3 GB | 7.8 GB | 5.0 GB | +1.7 GB |
| Q4_K_S | 6.0 GB | 7.5 GB | 4.8 GB | +2.0 GB |
| Q4_0 | 6.0 GB | 7.5 GB | 4.8 GB | +2.0 GB |
| Q3_K_M | 4.7 GB | 6.2 GB | 4.1 GB | +3.3 GB |
| Q3_K_S | 4.4 GB | 5.9 GB | 3.8 GB | +3.6 GB |
| Q2_K | 3.9 GB | 5.4 GB | 3.3 GB | +4.1 GB |
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Top-ranked open-source models that fit in 8.0GB.
FAQ
Can the NVIDIA RTX 5060 Ti 8GB run Qwen3 VL 8B Thinking?
Yes. The NVIDIA RTX 5060 Ti 8GB's 8.0GB of VRAM is enough to run Qwen3 VL 8B Thinking at Q5_K_M quantization (7.3GB required).
What's the best quantization to use?
Q5_K_M is the highest-precision quantization that fits in your 8.0GB. It uses about 7.3GB of memory and 8.8GB recommended for comfortable inference.
What if I need more headroom for context length?
KV cache memory grows with context length. The numbers above assume a baseline 2K-4K context. For long-context use (32K+), add another 2-6GB depending on the model architecture.